





import math

import flax.linen as nn
import jax.numpy as jnp

def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			List[str] = 1					,					_lowerCAmelCase			:			Optional[Any] = 1					,					_lowerCAmelCase			:			Optional[Any] = 1.0e4					,					_lowerCAmelCase			:			Dict = False					,					_lowerCAmelCase			:			Dict = 1.0					,					)			->    jnp.ndarray:
   assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
   assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
   A_       :					Union[str, Any]        =      float(embedding_dim // 2						)
   A_       :					List[str]        =      math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
   A_       :					Optional[Any]        =      min_timescale * jnp.exp(jnp.arange(a__					,					dtype=jnp.floataa						) * -log_timescale_increment						)
   A_       :					Union[str, Any]        =      jnp.expand_dims(a__					,					1						) * jnp.expand_dims(a__					,					0						)

   # scale embeddings
   A_       :					Optional[Any]        =      scale * emb

   if flip_sin_to_cos:
      A_       :					List[str]        =      jnp.concatenate([jnp.cos(a__						), jnp.sin(a__						)]					,					axis=1						)
   else:
      A_       :					Tuple        =      jnp.concatenate([jnp.sin(a__						), jnp.cos(a__						)]					,					axis=1						)
   A_       :					List[str]        =      jnp.reshape(a__					,					[jnp.shape(a__						)[0], embedding_dim]						)
   return signal


class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     __UpperCamelCase            =  32
     __UpperCamelCase            =  jnp.floataa



     @nn.compact
     def __call__(				self							:Tuple      ,   snake_case							:Dict	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_1"	)(lowercase_	)
        A_       :					Optional[Any]        =      nn.silu(lowercase_	)
        A_       :					List[str]        =      nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_2"	)(lowercase_	)
        return temb




class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     __UpperCamelCase            =  32
     __UpperCamelCase            =  False
     __UpperCamelCase            =  1



     @nn.compact
     def __call__(				self							:Any      ,   snake_case							:Tuple	):





        '''simple docstring'''




        return get_sinusoidal_embeddings(
            lowercase_      ,   embedding_dim=self.dim      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.freq_shift	)

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
   from .pipeline_kandinsky import KandinskyPipeline
   from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
   from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
   from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
   from .text_encoder import MultilingualCLIP




